Monitoring Water Quality of Inland Lakes by Remotely Sensed Observations Wei Yang Center for Environmental Remote Sensing Chiba University, Japan 2017-June-1

Outline Sect. 1 General introduction Sect.2 Enhanced Three-band Index for monitoring Chlorophyll-a Sect.3 SAMO-LUT algorithm for monitoring water constituent concentrations Sect. 4 General conclusions 2

Advantage of Satellite Remote Sensing Satellite remote sensing can provide synoptic observations of water area Waste Water 3

Physical Principle of Remote Sensing in Water Body SATELLITE Reflectance: R(λ) Bio-optical model Backward bb(λ) Inherent Optical Properties (IOP) Pure Water CDOM Tripton Phytoplankton Material Absorbed Target Water Area a(λ) Scattered b(λ) Specific Inherent Optical Properties (SIOP) = IOP/Concentration 4

Water Types: the Perspective of Remote Sensing Case I waters : waters are those dominated by phytoplankton (e.g. open oceans). Case II waters : waters are containing not only phytoplankton, but also non-phytoplankton suspended solids (i.e., tripton) and colored dissolved organic matters (CDOM) (e.g. coastal and inland waters). Chl-a can be satisfactorily estimated with satellite images (Gordon & Morel, 1983) Still challenging 5

Interactions between Different Difficulty for Monitoring Case II Waters Interactions between Different Components Case I waters Case II waters Phytoplankton Tripton CDOM 6

Reflectance spectra for case I and II waters Case I waters Case II waters Tripton = 90 g m-3 7

Review of Previous Studies for Remote Sensing in Case II Waters A number of methods have been proposed, e.g., Matrix Inversion Method (Hoogenboom et al., 1998), Band-ratio index (Gons, 1999; Ammenberg et al., 2002), Three-band index (Gitelson et al., 2008), and so on. The basic idea for all of the methods is to isolate the interactions between different components. Among these methods, the 3-band index significantly outperformed other indices (Gitelson et al., 2008). 8

Principle of the 3-band index: Absorption λ3≈740nm λ2≈ 708nm λ1≈ 665nm 9

Principle of the 3-band index: Backscattering λ2≈ 708nm λ3≈740nm λ1≈ 665nm 10

Principle of the 3-band index: Reflectance = 3-band Index Chl-a Constants 11

Problem of the 3-band index For waters with high turbidity (e.g. some Asian Lakes), the assumption is not reasonable anymore (Le et al., 2009). In these cases, the 3-band index becomes: Denominator includes the absorption not only phytoplankton, but also tripton and CDOM The existing methods still cannot provide satisfactory estimations for turbid case II waters. 12

Main objectives of my research To develop more effective methods to isolate the interactions between water components 1 2 To validate their performances using simulation, in situ and satellite datasets 13

An Enhanced 3-band Index for estimating Chl-a in turbid Case II waters Section 2 An Enhanced 3-band Index for estimating Chl-a in turbid Case II waters Sect. 2 14

Absorption properties for turbid water case (e.g. TSS=20 g m-3) λ2≈ 708nm λ1≈ 665nm λ3≈740nm Hints for new index 15

Development of the Enhanced 3-band Index Chl-a = l × New Index + m New Index Chl-a Constants 16

Study Areas Lake Dianchi, Lake Kasumigaura, China Japan Location: Yunnan Province, China Area: 300 ㎞2 Average depth：6 m Case II water: ・mixture of phytoplankton, tripton, and CDOM ・dominant species 　cyanobacteria Field survey: 4 times (Oct. 23, 2007; Jul. 15, 2008; Mar. 12, 2009; Jul.-Aug., 2009) Total 53 sites Lake Kasumigaura, Japan Location: Ibaraki Area: 171 ㎞2(western) Average depth：4 m Case II water: ・mixture of phytoplankton, tripton, and CDOM ・dominant species 　diatom（winter） 　cyanobacteria（summer） Field survey: 3 times (Feb. 18, 2006; Aug. 7, 2008; Sep. 1, 2009; routine field survey) total 61 sites 20km 30km 17 17

Data Collections Phytoplankton (Chl-a) Remote sensing reflectance Samples taken to lab; SCOR-UNESCO equations. Remote sensing reflectance Water-leaving radiance; Downward irradiance; Downward radiance of skylight. 18

Results for Lake Kasumigaura, Japan y = 282.18x + 29.12 R 2 = 0.82 20 40 60 80 100 120 -0.05 0.05 0.15 0.25 0.35 Original three-band index Measured Chl-a (mg m -3 ) (A) y = 161.24x + 28.04 R 2 = 0.90 20 40 60 80 100 120 -0.1 0.1 0.3 0.5 0.7 Enhanced three-band index Measured Chl-a (mg m -3 ) (B) Original 3-band Enhanced 3-band y = 0.55x + 27.98 R 2 = 0.48 20 40 60 80 100 120 Measured Chl-a (mg m -3 ) (C) Estimated Chl-a (mg m from original three-band index RMSE = 13.97 mg m MNB = - 4.53% NRMS = 19.01% y = 0.85x + 15.76 = 0.83 140 (D) from enhanced three-band index RMSE = 8.68 mg m MNB = 6.83% NRMS = 12.30% Original 3-band Enhanced 3-band 19

Results for Lake Dianchi, China y = 259.87x + 10.29 R 2 = 0.84 20 40 60 80 100 120 140 160 0.1 0.2 0.3 0.4 0.5 Original three-band index Measured Chl-a (mg m -3 ) (A) y = 119.57x + 10.26 R 2 = 0.91 20 40 60 80 100 120 140 160 0.25 0.5 0.75 1 1.25 Enhanced three-band index Measured Chl-a (mg m -3 ) (B) Original 3-band Enhanced 3-band y = 0.58x + 35.72 R 2 = 0.74 50 100 150 200 250 300 350 Measured Chl-a (mg m -3 ) (C) Estimated Chl-a (mg m from original three-band index RMSE = 41.29 mg m MNB = 0.75% NRMS = 35.83% y = 0.89x + 8.67 = 0.96 (D) from enhanced three-band index RMSE = 15.82 mg m MNB = - 3.26% NRMS = 21.43% Original 3-band Enhanced 3-band 20

Section 3 Estimation of water constituent concentrations in case II waters by semi-analytical model-optimizing and look-up-tables (SAMO-LUT) Sect. 3 Objective: To provide information of not only Chl-a, but also tripton and CDOM. 21

Selection of Three Previous Semi-analytical Models Chl-a estimation model (3-band index): (Gitelson et al., 2008) Tripton estimation model: (Ammenberg et al., 2002) CDOM estimation model: (Ammenberg et al., 2002) B5, B7, B9, and B10 denote MERIS bands 5 (555-565 nm), 7 (660-670 nm), 9 (703-713 nm), and 10 (750-757 nm), respectively 22

Simulation test for re-analyzing previous models Remote-sensing reflectance generated from: [Chl-a] ranges： 1-300 mg m-3; [Tr] ranges： 1-250 g m-3; [CDOM] ranges： 0.1-10 m-1. In total, 19,964 samples for regression analysis 23

Problem for previous model (Chl-a vs. 3-band index) Chl-a=300 mg m-3 24

Problem for previous model (Tripton vs. Rrs(B10)) 25

Problem for previous model (CDOM Vs. Rrs(b7)/ Rrs(b5)) 26

Development of the new algorithm Semi-Analytical Model-Optimizing and Look-Up Table (SAMO-LUT) Basic idea of this new method is an imaginary case II water, in which only one component changes while the other two are controlled as constants. E.g. Tr and CDOM are constants; only Chl-a is changing : Constants Rrs is changing only with Chl-a. Possibility for improving three-band index 27

Example of tripton and CDOM controlled as constants (e. g. Chl-a vs Example of tripton and CDOM controlled as constants (e.g. Chl-a vs. 3-band index) (I)Tr=1, CDOM=0.1 (II)Tr=110, CDOM=2 (III)Tr=250, CDOM=10 28

Construction of Look-up Tables (LUT) using simulation data The coefficients of Chl-a model for each combination of Tripton and CDOM are stored in the LUT. The practical problem is how to find the optimal models for a given pixel? 29

Iteration strategy in the SAMO-LUT method (Step 1: getting initial values) 30

Repeat ! Chla0 = Chla1 Tr0 = Tr1 Iteration strategy in the SAMO-LUT method (Step 2: optimizing estimation model) Repeat ! Chla0 = Chla1 Tr0 = Tr1 31

Ending the iteration by difference between the current and last iterations 10 times 32

Validation results: By simulated data (noise-free) RMSE （0.43,0.42,0.06） 33

Validation results: By in situ data ( low noise) RMSE （3.4,1.8,0.2） 34

Validation results: by MERIS data ( high noise) RMSE (8.8, 6.9, 0.25) 35

Spatial Distribution of Water Quality Kunming City Weak Wind Strong Wind 36

Long-term Monitoring in Lake Kasumigaura (Matsushita, Jaelania, Yang et al., 2015, RSSJ) Time-series of satellite estimation show high agreements with field measurements.

Conclusions: my proposed algorithms 38

Take-home Messages Scientists in Chiba University, Japan are working on satellite monitoring of water quality. Our proposed algorithm worked well in long-term monitoring of water quality parameters in Japan’s lakes. We are challenging the continental/global monitoring of inland lakes, to aid the conventional investigations. 39

Thank you!